@misc{BoissierKurzynski2018,
  author    = {Boissier, Martin and Kurzynski, Daniel},
  title     = {Workload-Driven Horizontal Partitioning and Pruning for Large HTAP Systems},
  series = {2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)},
  journal   = {2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-6306-6},
  doi       = {10.1109/ICDEW.2018.00026},
  pages     = {116 -- 121},
  year      = {2018},
  abstract  = {Modern server systems with large NUMA architectures necessitate (i) data being distributed over the available computing nodes and (ii) NUMA-aware query processing to enable effective parallel processing in database systems. As these architectures incur significant latency and throughout penalties for accessing non-local data, queries should be executed as close as possible to the data. To further increase both performance and efficiency, data that is not relevant for the query result should be skipped as early as possible. One way to achieve this goal is horizontal partitioning to improve static partition pruning. As part of our ongoing work on workload-driven partitioning, we have implemented a recent approach called aggressive data skipping and extended it to handle both analytical as well as transactional access patterns. In this paper, we evaluate this approach with the workload and data of a production enterprise system of a Global 2000 company. The results show that over 80\% of all tuples can be skipped in average while the resulting partitioning schemata are surprisingly stable over time.},
  language  = {en}
}
@misc{SchlosserKossmannBoissier2019,
  author    = {Schlosser, Rainer and Kossmann, Jan and Boissier, Martin},
  title     = {Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies},
  series = {2019 IEEE 35th International Conference on Data Engineering (ICDE)},
  journal   = {2019 IEEE 35th International Conference on Data Engineering (ICDE)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-7474-1},
  issn      = {1084-4627},
  doi       = {10.1109/ICDE.2019.00113},
  pages     = {1238 -- 1249},
  year      = {2019},
  abstract  = {An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality.},
  language  = {en}
}
@misc{SerthPodlesnyBornsteinetal.2017,
  author    = {Serth, Sebastian and Podlesny, Nikolai and Bornstein, Marvin and Lindemann, Jan and Latt, Johanna and Selke, Jan and Schlosser, Rainer and Boissier, Martin and Uflacker, Matthias},
  title     = {An interactive platform to simulate dynamic pricing competition on online marketplaces},
  series = {2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC)},
  journal   = {2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC)},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {New York},
  isbn      = {978-1-5090-3045-3},
  issn      = {2325-6354},
  doi       = {10.1109/EDOC.2017.17},
  pages     = {61 -- 66},
  year      = {2017},
  abstract  = {E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.},
  language  = {en}
}